1. Field of the Invention
The present invention relates to image segmentation, and more particularly to a system and method for image segmentation implementing an interactive level set.
2. Discussion of Related Art
Image segmentation approaches can be classified as boundary or region-based. Boundary-driven techniques rely on the generation of a strength image and the extraction of prominent edges, while region-based methods rely on the homogeneity of spatially localized features and properties. Snake-driven techniques are typically the most appropriate tool to derive boundary-based methods. A curve propagation technique is a common way to implement such terms.
To this end, a parameter space that defines a curve in the image plane is considered. Object extraction is equivalent with finding the lowest potential of an objective function. Such a function involves internal and external terms. The internal term enforces some desired geometric characteristics of the curve, while the external term moves the curve to the desired image features. Level set methods address such an objective in various application domains.
Level set formulations consider the problem in a higher dimension and represent the evolving curve as the zero-level set of an embedding function. The evolution of this function can then be derived in a straightforward manner from the original flow that guides the propagation of the curve. Such methods are implicit, intrinsic and topology free leading to a natural handling of important shape deformations.
An important limitation of level set formulations is sensitivity to noise and failing to capture/encode prior knowledge shape-driven on the structure to be recovered. A geometric flow that evolves the solution closer to the prior can introduce prior shape knowledge within the segmentation process. A more elegant formulation was derived in which such constraints were introduced in the form of energy components that constrain the solution space.
User-interaction is an important component in medical segmentation where boundary-tracing tools are implemented. User interaction can be considered as a different form of prior knowledge to be added in the segmentation process. Recent advances in medical imaging have increased the accuracy of automated techniques. However, clinical users typically need to correct their outcome. Although, level set methods are an established segmentation technique in medical imaging they do not support user interaction.